RLoptimal: Optimal Adaptive Allocation Using Deep Reinforcement Learning

An implementation to compute an optimal adaptive allocation rule using deep reinforcement learning in a dose-response study (Matsuura et al. (2022) <doi:10.1002/sim.9247>). The adaptive allocation rule can directly optimize a performance metric, such as power, accuracy of the estimated target dose, or mean absolute error over the estimated dose-response curve.

Version:	1.2.0
Imports:	DoseFinding, glue, R6, reticulate, stats, utils
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
Published:	2024-12-21
DOI:	10.32614/CRAN.package.RLoptimal
Author:	Kentaro Matsuura [aut, cre, cph], Koji Makiyama [aut, ctb]
Maintainer:	Kentaro Matsuura <matsuurakentaro55 at gmail.com>
BugReports:	https://github.com/MatsuuraKentaro/RLoptimal/issues
License:	MIT + file LICENSE
URL:	https://github.com/MatsuuraKentaro/RLoptimal
NeedsCompilation:	no
Language:	en-US
Materials:	README NEWS
CRAN checks:	RLoptimal results

Documentation:

Reference manual:	RLoptimal.pdf
Vignettes:	Optimal Adaptive Allocation Using Deep Reinforcement Learning (source, R code)

Downloads:

Package source:	RLoptimal_1.2.0.tar.gz
Windows binaries:	r-devel: RLoptimal_1.2.0.zip, r-release: RLoptimal_1.2.0.zip, r-oldrel: RLoptimal_1.2.0.zip
macOS binaries:	r-release (arm64): RLoptimal_1.2.0.tgz, r-oldrel (arm64): RLoptimal_1.2.0.tgz, r-release (x86_64): RLoptimal_1.2.0.tgz, r-oldrel (x86_64): RLoptimal_1.2.0.tgz
Old sources:	RLoptimal archive

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